6 papers
Exploiting Exact Conditionals Improves Conditioning: Provably Fast Mixing Time Bounds By Sampling from the Marginal
Abhijit Chowdhary, Federica Milinanni, Julianne Chung +1
The problem of sampling from a probability distribution arises in many applications such as posterior sampling in hierarchical Bayesian inverse problems and Gaussian processes for…
A Majorization-Minimization with Monte Carlo Approach for Hyperparameter Estimation
Elle Buser, Julianne Chung, Hugo Díaz +1
We consider inverse problems with linear forward models and Gaussian priors, but with unknown hyperparameters that may arise from the model, the noise, or the specification of the…
Efficient sampling approaches based on generalized Golub-Kahan methods for large-scale hierarchical Bayesian inverse problems
Elle Buser, Julianne Chung
Uncertainty quantification for large-scale inverse problems remains a challenging task. For linear inverse problems with additive Gaussian noise and Gaussian priors, the posterior…
Efficient hyperparameter estimation in Bayesian inverse problems using sample average approximation
Julianne Chung, Scot M. Miller, Malena Sabate Landman +1
In Bayesian inverse problems, it is common to consider several hyperparameters that define the prior and the noise model that must be estimated from the data. In particular, we are…
Inexact Generalized Golub-Kahan Methods for Large-Scale Bayesian Inverse Problems
Yutong Bu, Julianne Chung
Solving large-scale Bayesian inverse problems presents significant challenges, particularly when the exact (discretized) forward operator is unavailable. These challenges often ari…
Efficient iterative methods for hyperparameter estimation in large-scale linear inverse problems
Khalil A Hall-Hooper, Arvind K Saibaba, Julianne Chung +1
We study Bayesian methods for large-scale linear inverse problems, focusing on the challenging task of hyperparameter estimation. Typical hierarchical Bayesian formulations that fo…